In [3]:

    

# Linear SVM

import numpy as np
from sklearn import datasets
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC

iris = datasets.load_iris()

X = iris['data'][:,(2,3)]
y = (iris['target'] == 2).astype(np.float64)

svm_clf = Pipeline([
    ('scaler', StandardScaler()),
    ('linear_svc', LinearSVC(C=1,loss='hinge')),
])

svm_clf.fit(X, y)

svm_clf.predict([[5.5, 1.7]])


    

    
Out[3]:





array([1.])

In [5]:

    

# Nonlinear SVM

from sklearn.datasets import make_moons
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.svm import LinearSVC

polynomial_svm_clf = Pipeline([
    ('poly_features', PolynomialFeatures(degree=3)),
    ('scaler', StandardScaler()),
    ('linear_svc', LinearSVC(C=10,loss='hinge')),
])

polynomial_svm_clf.fit(X,y)


    

    
Out[5]:





Pipeline(memory=None,
     steps=[('poly_features', PolynomialFeatures(degree=3, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=10, class_weight=None, dual=True, fit_intercept=True,
     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',
     penalty='l2', random_state=None, tol=0.0001, verbose=0))])

In [7]:

    

# Polynomial Kernel

from sklearn.svm import SVC

poly_kernel_clf = Pipeline([
    ('scaler', StandardScaler()),
    ('svc', SVC(kernel='poly', degree=3,coef0=1, C=5)),
])

poly_kernel_clf.fit(X,y)


    

    
Out[7]:





Pipeline(memory=None,
     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=5, cache_size=200, class_weight=None, coef0=1,
  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
  kernel='poly', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False))])

In [9]:

    

rbf_kernel_clf = Pipeline([
    ('scaler', StandardScaler()),
    ('svc', SVC(kernel='rbf', gamma=5, C=0.001)),
])

rbf_kernel_clf.fit(X,y)


    

    
Out[9]:





Pipeline(memory=None,
     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma=5, kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False))])

In [15]:

    

# SVM Regression

from sklearn.svm import LinearSVR

svm_reg = LinearSVR(epsilon=1.5)
svm_reg.fit(X,y)

from sklearn.svm import SVR

svm_reg = SVR(kernel='poly', gamma ='scale', degree=2, C=100, epsilon=0.1)
svm_reg.fit(X,y)


    

    
Out[15]:





SVR(C=100, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='scale',
  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)

In [ ]: